Future of functional testing: AI, ML and automation technologies with sustainable solutions

As we kickstart 2024, the focus is on several new trends and advancements while also evaluating the processes of the previous year. We tend to gauge what worked and what went wrong while focusing on what can be improved. This brings us to testing processes and technologies, which is a vital practise across industries. Particularly, functional testing is an essential process in equipment manufacturing.

Functional testing is commonly described as the evaluation and validation of machinery, systems, or equipment in a production environment. It is a critical step that ensures equipment operates as intended while meeting specified requirements. Successful functional testing means equipment performs its intended functions accurately, efficiently, and without glitches.

Several aspects are covered in the areas of functional testing on production equipment. For one, clearly defined functional specifications can define the way forward in the testing process. This includes outlining the expected behaviour and performance criteria, serving as a baseline for further testing and validation. Over the years, several new and advanced technologies have taken centre stage in functional testing. As we step into 2024, technologies like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Industry 4.0, Robotics and much more come into the limelight.

The integration of AI and ML tools in functional testing has brought about significant improvements in efficiency, accuracy, and the overall effectiveness of testing processes. This is clearly seen in testing automation where these technologies can enable intelligent test automation frameworks, generate and execute test scripts, reduce the need for manual effort, and finally identify and rectify failures.

More importantly, AI and ML play a crucial role in prediction and analysis in the testing process. Machine learning models can predict potential defects by analysing historical data, code complexity, and testing metrics, paving the way for us to focus on high-risk areas and make better use of resources. At the same time, AI can predict potential issues in test environments by monitoring system performance, aiding in proactive maintenance and reducing downtime during testing.

The application of AI and ML tools is evident across industries today. We can see that several new firms and enterprises are upping their game with the adoption of AI and ML technologies. Be it increasing security testing tools, natural language processing, or simulating realistic user behaviour in performance testing – we’ve seen a vast adoption of AI and ML as a means to the future.

Within future tech, one is bound to come across Industry 4.0, which is often synonymous with smart manufacturing and is also referred to as the fourth industrial revolution. Industry 4.0 brings in the digital transformation of an industry, with technologies like the IoT and AI. In the context of functional testing, Industry 4.0 certainly has a profound impact as it is transforming the way testing is conducted.

One of the prominent factors of Industry 4.0 is the widespread use of connected devices and sensors. Functional and modern testing processes include verification and validation of IoT devices, ensuring seamless communication and functionality within a connected ecosystem. This also brings us to the Digital Twin testing process, where a virtual model is designed to accurately mirror a physical product.

Industry 4.0 and Digital Twin go hand-in-hand, especially in testing processes, which include simulation testing to validate and test system behaviour in a virtual environment before physical implementation. Using Digital Twin in Industry 4.0 is crucial as it paves the way for the early identification of issues and reduces the risks and costs of physical testing.

With regard to physical testing, automation and robotics are other factors that can’t be overlooked. Both these technologies bring in several advantages in increasing efficiency, accuracy and enhancing the overall effectiveness of the testing process. For instance, advanced automation technologies help in the creation of reusable scripts that can be applied across various builds, products and releases. Moreover, these technologies also help in modularising test scripts, and huge volumes of reusable components can be created.

Enhanced efficiency and accuracy create time for increased test coverage. When we automate testing, the overall process can cover a larger number of test scenarios in less time. As far as functional testing in 2024 is concerned, automation and robotics technologies play a vital role in comprehensive test coverage, identifying defects and also ensuring a better quality of the product.

At the same time, industries worldwide are increasingly focusing on green practices and sustainability solutions in the testing process. Adopting green testing practises aligns with broader sustainability goals and contributes to minimising the carbon footprint. While green testing practises are still budding, one can expect a definitive surge in the coming years.

One of the key factors of sustainable testing is bringing in energy-efficient test environments by using sustainable hardware and ensuring that test environments are configured to minimise power consumption when not actively in use. This is most commonly achieved with cloud services as it reduces the need for on-premises infrastructure. Cloud providers often have energy-efficient data centres, popularly called ‘green data centres’, and can dynamically allocate resources based on demand, optimising energy usage.

It’s evident that green practises and sustainable solutions are the way forward for testing solutions across industries. AI, ML and automation will continue to lead the forefront of functional testing, at least for the next few years. At the same time, eco-friendly principles with environmental sustainability will also be a key focus area for testing as well as other aspects of industrial development.

Author: Pandarinath Siddineni, Domain Head, Systems & Software.